Rice disease classification based on leaf image using multilevel Support Vector Machine (SVM)

Dwi Ratna Sulistyaningrum*, Alima Rasyida, Budi Setiyono

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)

Abstract

Plant disease is one of many factors that decrease the quality and quantity value of agriculture, especially rice plants. Automatic technology based on digital image processing is being developed to overcome this problem. Support Vector Machine (SVM) is one of the most used classifications and detection methods. SVM has been developed into multi SVM by combining several binary SVMs to classify more than two classes. In the proposed system, we use one of the multi SVM strategy, namely One Vs. All. The accuracy of classification reaches 86.10% using linear kernel. It has a higher value of accuracy than using polynomial and RBF kernel function. The scenario for the number of the dataset used is 70% for the training set and 30% for the testing set from a whole 240 images.

Original languageEnglish
Article number012053
JournalJournal of Physics: Conference Series
Volume1490
Issue number1
DOIs
Publication statusPublished - 9 Jun 2020
Event5th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2019 - Surabaya, Indonesia
Duration: 19 Oct 2019 → …

Fingerprint

Dive into the research topics of 'Rice disease classification based on leaf image using multilevel Support Vector Machine (SVM)'. Together they form a unique fingerprint.

Cite this